
import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = None					,				__SCREAMING_SNAKE_CASE = None					,				__SCREAMING_SNAKE_CASE = None					,				):
	if config_name_or_path is None:
		lowercase	    =       'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

	if generator_tokenizer_name_or_path is None:
		lowercase	    =       generator_name_or_path

	if question_encoder_tokenizer_name_or_path is None:
		lowercase	    =       question_encoder_name_or_path

	lowercase	    =       RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

	# Save model.
	lowercase	    =       RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)

	lowercase	    =       gen_config
	lowercase	    =       question_encoder_config

	lowercase	    =       model_class.from_pretrained_question_encoder_generator(
	    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				config=__SCREAMING_SNAKE_CASE				)
	rag_model.save_pretrained(__SCREAMING_SNAKE_CASE				)

	# Sanity check.
	model_class.from_pretrained(__SCREAMING_SNAKE_CASE				)

	# Save tokenizers.
	lowercase	    =       AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
	gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
	lowercase	    =       AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
	question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'				)


if __name__ == "__main__":
	UpperCAmelCase       =					argparse.ArgumentParser()
	parser.add_argument(
	    '''--model_type''',
	    choices=['''rag_sequence''', '''rag_token'''],
	    required=True,
	    type=str,
	    help='''RAG model type: rag_sequence, rag_token''',
	)
	parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
	parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
	parser.add_argument(
	    '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
	)

	parser.add_argument(
	    '''--generator_tokenizer_name_or_path''',
	    type=str,
	    help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
	)
	parser.add_argument(
	    '''--question_encoder_tokenizer_name_or_path''',
	    type=str,
	    help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
	)
	parser.add_argument(
	    '''--config_name_or_path''',
	    type=str,
	    help=(
	        '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
	        ''' ``model_type``'''
	    ),
	)

	UpperCAmelCase       =					parser.parse_args()

	UpperCAmelCase       =					Path(args.dest)
	dest_dir.mkdir(exist_ok=True)

	consolidate(
	    args.model_type,
	    args.generator_name_or_path,
	    args.question_encoder_name_or_path,
	    dest_dir,
	    args.config_name_or_path,
	    args.generator_tokenizer_name_or_path,
	    args.question_encoder_tokenizer_name_or_path,
	)






'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
			from .pipeline_unclip import UnCLIPPipeline
			from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
			from .text_proj import UnCLIPTextProjModel




